Library Header Image
LSE Research Online LSE Library Services

A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization

Lahmiri, Salim, Bekiros, Stelios and Avdoulas, Christos (2023) A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization. Decision Analytics Journal, 6. ISSN 2772-6622

Full text not available from this repository.
Identification Number: 10.1016/j.dajour.2023.100166


The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested in such predictive systems to improve their buying and lending decisions and activities. This study explores the application of artificial intelligence, machine learning, and nonlinear statistical models to house price prediction problems. In that order, we use boosting ensemble regression trees, support vector regression, and Gaussian process regression. Bayesian optimization is implemented in a ten-fold cross-validation framework to determine their respective optimal kernels and parameter values. Four performance metrics are used to evaluate the prediction ability of each predictive system. The experimental results showed that boosting ensemble regression trees performed the best, followed by Gaussian process regression and support vector regression. In addition, all three aforementioned predictive systems outperformed artificial neural networks and multi-variate regression employed in recent work on the same data set. Under this perspective, it is concluded that boosting ensemble regression trees are clear candidates to be considered for operational house price prediction in Taiwan.

Item Type: Article
Additional Information: © 2023 The Author(s).
Divisions: LSE
Subjects: Q Science > QA Mathematics
Date Deposited: 10 Feb 2023 12:18
Last Modified: 26 May 2024 07:21

Actions (login required)

View Item View Item